In [1]:
if (!require("plotly")) install.packages("plotly")
library(plotly)

Loading required package: plotly
Warning message in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
“there is no package called ‘plotly’”also installing the dependency ‘crosstalk’

Updating HTML index of packages in '.Library'
Making 'packages.html' ... done
Loading required package: ggplot2

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout
In [2]:
if (!require("ggplot2")) install.packages("ggplot2")
library(ggplot2)
In [3]:
if (!require("psych")) install.packages("psych")
library(psych)

Loading required package: psych
Warning message in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
“there is no package called ‘psych’”also installing the dependency ‘mnormt’

Updating HTML index of packages in '.Library'
Making 'packages.html' ... done

Attaching package: ‘psych’

The following objects are masked from ‘package:ggplot2’:

    %+%, alpha
In [4]:
if (!require("rgl")) install.packages("rgl")
library(psych)

Loading required package: rgl
Warning message in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
“there is no package called ‘rgl’”also installing the dependencies ‘webshot’, ‘manipulateWidget’

Warning message in install.packages("rgl"):
“installation of package ‘rgl’ had non-zero exit status”Updating HTML index of packages in '.Library'
Making 'packages.html' ... done
In [5]:
if (!require("RColorBrewer")) install.packages("RColorBrewer")
library(RColorBrewer)

Loading required package: RColorBrewer
In [6]:
if (!require("corrplot")) install.packages("corrplot")
library(corrplot)

Loading required package: corrplot
Warning message in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
“there is no package called ‘corrplot’”Updating HTML index of packages in '.Library'
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corrplot 0.84 loaded
In [7]:
if (!require("MASS")) install.packages("MASS")
library(MASS)

Loading required package: MASS

Attaching package: ‘MASS’

The following object is masked from ‘package:plotly’:

    select
In [8]:
if (!require("plot3D")) install.packages("plot3D")
library(MASS)

Loading required package: plot3D
Warning message in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
“there is no package called ‘plot3D’”also installing the dependency ‘misc3d’

Updating HTML index of packages in '.Library'
Making 'packages.html' ... done
In [9]:
if (!require("lattice")) install.packages("lattice")
library(lattice)

Loading required package: lattice
In [10]:
if (!require("reshape")) install.packages("reshape")
library(lattice)

Loading required package: reshape
Warning message in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
“there is no package called ‘reshape’”Updating HTML index of packages in '.Library'
Making 'packages.html' ... done

1) ------------------------ Iris Dataset ------------------------

In [11]:
head(iris)
Sepal.LengthSepal.WidthPetal.LengthPetal.WidthSpecies
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5.0 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa
In [12]:
head(Orange)
Treeagecircumference
1 118 30
1 484 58
1 664 87
1 1004115
1 1231120
1 1372142
In [13]:
head(mpg)
manufacturermodeldisplyearcyltransdrvctyhwyflclass
audi a4 1.8 1999 4 auto(l5) f 18 29 p compact
audi a4 1.8 1999 4 manual(m5)f 21 29 p compact
audi a4 2.0 2008 4 manual(m6)f 20 31 p compact
audi a4 2.0 2008 4 auto(av) f 21 30 p compact
audi a4 2.8 1999 6 auto(l5) f 16 26 p compact
audi a4 2.8 1999 6 manual(m5)f 18 26 p compact

2) ------------------------ Scatter Plots ------------------------

2.1 -

In [61]:
?plot
In [64]:
plot(iris$Sepal.Length, iris$Sepal.Width, pch=21, main="Iron man", type = "b", xlab = "Xdirector")

2.2 -

In [65]:
plot(iris$Petal.Length, iris$Sepal.Width, 
     pch=21, bg=c("red","green","blue")[unclass(iris$Species)], 
     main="Second Scatter Plot - Iris Data")

2.3 -

In [16]:
qplot(iris$Sepal.Length, iris$Sepal.Width)

2.4 -

In [17]:
scatter <- ggplot(data=iris, aes(x = Sepal.Length, y = Sepal.Width)) 
scatter + geom_point(aes(color=Species, shape=Species)) +
  xlab("Sepal Length") +  ylab("Sepal Width") +
  ggtitle("Sepal Length-Width")

2.5 -

In [18]:
ggplot (iris, aes (x = Sepal.Length, y = Sepal.Width, colour = Species)) + stat_density2d ()

2.6 -

In [19]:
pal <- c("red", "blue", "green")
pal <- setNames(pal, c("virginica", "setosa", "versicolor"))
plot_ly(data = iris, x = ~Sepal.Length, y = ~Sepal.Width, color = ~Species, colors = pal)

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

2.7 - Plotly (3D interactive)

In [20]:
with(iris, plot_ly(iris, x = Sepal.Length, y= Petal.Length, z = Sepal.Width,
                  size = Petal.Width, color = Species,
                  type="scatter3d", mode="markers"))

Warning message:
“`line.width` does not currently support multiple values.”Warning message:
“`line.width` does not currently support multiple values.”Warning message:
“`line.width` does not currently support multiple values.”

2.8 scatterplot3d

In [21]:
if (!require("scatterplot3d")) install.packages("scatterplot3d")
library(scatterplot3d)

Loading required package: scatterplot3d
Warning message in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
“there is no package called ‘scatterplot3d’”Updating HTML index of packages in '.Library'
Making 'packages.html' ... done
In [22]:
scatterplot3d(iris[,1:3], color=as.integer(iris$Species))

2.9 lattice

In [23]:
library(lattice)
xyplot(Sepal.Length ~ Sepal.Width, iris, groups = iris$Species, pch= 20)
In [24]:
xyplot(Sepal.Width ~ Sepal.Length | Species, iris, groups = iris$Species, pch= 20)
In [25]:
cloud(Sepal.Length ~ Petal.Length * Petal.Width, data = iris,
      groups = Species, screen = list(z = 20, x = -70),
      perspective = FALSE,
      key = list(title = "Iris Data", x = .15, y=.85, corner = c(0,1),
                 border = TRUE,
                 points = Rows(trellis.par.get("superpose.symbol"), 1:3),
                 text = list(levels(iris$Species))))

3) ------------------------ Pairwise Scatter Plots ------------------------

3.1 -- Basic R

In [26]:
pairs(iris[1:4], main = "Iris Data", pch = 21, 
      bg = c("red", "green3", "blue")[unclass(iris$Species)])

3.2 -- Basic R

In [27]:
pairs(iris[1:4], main = "Iris Data", pch = 21, 
      bg = c("red", "green3", "blue")[unclass(iris$Species)],
      lower.panel = NULL)

3.3 -- Basic R

In [28]:
my_cols <- c("#00AFBB", "#E7B800", "#FC4E07")
pairs(iris[,1:4], pch = 19,  cex = 0.5,
      col = my_cols[iris$Species],
      lower.panel=NULL)

3.4 -- Basic R

In [29]:
library(psych)
pairs.panels(iris[,-5], 
             method = "pearson", # correlation method
             hist.col = "#00AFBB",
             density = TRUE,  # show density plots
             ellipses = TRUE # show correlation ellipses
)

3.5 -- ggplot along columns

In [30]:
facet <- ggplot(data=iris, aes(Sepal.Length, y=Sepal.Width, color=Species)) + 
  geom_point(aes(shape=Species), size=1.5) + geom_smooth(method="lm") +
  xlab("Sepal Length") + ylab("Sepal Width") + ggtitle("Faceting")
# Along rows
facet + facet_grid(. ~ Species)

3.6 -- ggplot along rows

In [31]:
facet <- ggplot(data=iris, aes(Sepal.Length, y=Sepal.Width, color=Species)) + 
  geom_point(aes(shape=Species), size=1.5) + geom_smooth(method="lm") +
  xlab("Sepal Length") + ylab("Sepal Width") + ggtitle("Faceting")
# Along columns
facet + facet_grid(Species ~ .)

4) ------------------------ Histograms ------------------------

4.1 -- Base R

In [32]:
hist(iris$Petal.Length, freq=FALSE, col="green", 
     xlab="Petal Length", main="Colored histogram")
curve(dnorm(x, mean=mean(iris$Petal.Length), 
            sd=sd(iris$Petal.Length)), add=TRUE, col="red") #line

4.2 -- ggplot

In [33]:
ggplot(iris, aes(x=Petal.Length, y=..density..)) +
  geom_histogram(fill="cornsilk", binwidth=0.2, colour="grey60", size=.2) +
  geom_density()

4.3 -- ggplot overlay

In [34]:
qplot(Sepal.Length, data=iris, geom="histogram", fill=Species, binwidth=0.2)

4.3 -- plotly

In [35]:
plot_ly(alpha = 0.6) %>%
 add_histogram(x = ~iris$Petal.Length) %>%
 add_histogram(x = ~iris$Sepal.Length) %>%
 layout(barmode = "overlay")

5) ------------------------ Pie Charts ------------------------

5.1 -- Base R

In [36]:
data = c(179718,41370,41914,44280)
pct = (data/sum(data))*100
pct = round(pct,2)
labels = c("Army", "Navy", "Air Force","Marines")
labels = paste(labels,pct, "%")
col = c("purple", "violetred1", "green3","red", "cyan")
In [37]:
pie(pct,col = col, radius = 1, init.angle = 90, clockwise = TRUE, 
    labels =labels, main = "Traumatic Brain Injury 2000-2014(Q2)")

6) ------------------------ Bar Graphs ------------------------

6.1 -- Base R

In [38]:
library(RColorBrewer)
barplot(table(iris$Species,iris$Sepal.Length),col  = brewer.pal(3,"Set1")) #Stacked Plot

6.2 -- ggplot

In [39]:
library(reshape2)
iris2 <- melt(iris, id.vars="Species")
iris2[1:3,]
bar1 <- ggplot(data=iris2, aes(x=Species, y=value, fill=variable))
bar1 + geom_bar(stat="identity", position="dodge") + 
  scale_fill_manual(values=c("orange", "blue", "darkgreen", "purple"),
                    name="Iris\nMeasurements",
                    breaks=c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"),
                    labels=c("Sepal Length", "Sepal Width", "Petal Length", "Petal Width"))
Speciesvariablevalue
setosa Sepal.Length5.1
setosa Sepal.Length4.9
setosa Sepal.Length4.7

6.2 -- plotly

In [40]:
plot_ly(iris, x = ~Sepal.Length, y = ~Sepal.Width, type = 'bar', name = 'Sepal.Width') %>%
 add_trace(y = ~Petal.Length, name = 'Petal.Length') %>%
 add_trace(y = ~Petal.Width, name = 'Petal.Width')

6.3 -- plotly

In [41]:
plot_ly(iris, x = ~Sepal.Length, y = ~Sepal.Width, type = 'bar', name = 'Sepal.Width') %>%
 add_trace(y = ~Petal.Length, name = 'Petal.Length') %>%
 add_trace(y = ~Petal.Width, name = 'Petal.Width') %>%
 layout(yaxis = list(title = 'Count'), barmode = 'stack')

7) ------------------------ Line Graphs ------------------------

7.1 Basic R

In [42]:
ind1=which(Orange$Tree=="1")
ind2=which(Orange$Tree=="2")
ind3=which(Orange$Tree=="3")
ind4=which(Orange$Tree=="4")
ind5=which(Orange$Tree=="5")
plot(Orange$age[ind1],Orange$circumference[ind1],main="Orange tree growth",
     xlab="Age",ylab="Circumference",type="l",cex.lab=1.25,cex.axis=0.8,xlim=c(100,1600),
     ylim=c(25,215))
lines(Orange$age[ind2],Orange$circumference[ind2],col=2)
lines(Orange$age[ind3],Orange$circumference[ind3],col=3)
lines(Orange$age[ind4],Orange$circumference[ind4],col=4)
lines(Orange$age[ind5],Orange$circumference[ind5],col=5,lty=2)
legend(100,180,legend=c("Tree 1","Tree 2","Tree 3","Tree 4","Tree 5"),col=1:5,lty=c(rep(1,4),2))

7.2 ggplot

In [43]:
qplot(age, circumference, data = Orange, geom = c("point", "line"), color = Tree)

7.3 plotly

In [44]:
ind1=which(Orange$Tree=="1")
ind2=which(Orange$Tree=="2")
ind3=which(Orange$Tree=="3")
ind4=which(Orange$Tree=="4")
ind5=which(Orange$Tree=="5")
p<- plot_ly(Orange, x = ~age[ind1], y = ~circumference[ind1], name = 'Tree 1', type = 'scatter',
       mode = 'lines') %>%
 add_trace(y = ~circumference[ind2], name = 'Tree 2', mode = 'lines+markers') %>%
 add_trace(y = ~circumference[ind3], name = 'Tree 3', mode = 'lines+markers') %>%
 add_trace(y = ~circumference[ind4], name = 'Tree 4', mode = 'lines+markers') %>%
 add_trace(y = ~circumference[ind5], name = 'Tree 5', mode = 'lines+markers')
p

8) ------------------------ Heatmaps ------------------------

8.1 one color

In [45]:
nba <- read.csv("http://datasets.flowingdata.com/ppg2008.csv")
nba$Name <- with(nba, reorder(Name, PTS))
# scaling <- function(x) { (x - min(x))/(max(x) - min(x))} 
normed <- as.data.frame(lapply(nba[,-1], rescale)) 
normed$Name <- nba$Name
nba.m <- melt(normed)

Using Name as id variables
In [46]:
head(nba)
NameGMINPTSFGMFGAFGPFTMFTAFTP⋯X3PAX3PPORBDRBTRBASTSTLBLKTOPF
Dwyane Wade 79 38.6 30.2 10.8 22.0 0.491 7.5 9.8 0.765 ⋯ 3.5 0.317 1.1 3.9 5.0 7.5 2.2 1.3 3.4 2.3
LeBron James 81 37.7 28.4 9.7 19.9 0.489 7.3 9.4 0.780 ⋯ 4.7 0.344 1.3 6.3 7.6 7.2 1.7 1.1 3.0 1.7
Kobe Bryant 82 36.2 26.8 9.8 20.9 0.467 5.9 6.9 0.856 ⋯ 4.1 0.351 1.1 4.1 5.2 4.9 1.5 0.5 2.6 2.3
Dirk Nowitzki 81 37.7 25.9 9.6 20.0 0.479 6.0 6.7 0.890 ⋯ 2.1 0.359 1.1 7.3 8.4 2.4 0.8 0.8 1.9 2.2
Danny Granger 67 36.2 25.8 8.5 19.1 0.447 6.0 6.9 0.878 ⋯ 6.7 0.404 0.7 4.4 5.1 2.7 1.0 1.4 2.5 3.1
Kevin Durant 74 39.0 25.3 8.9 18.8 0.476 6.1 7.1 0.863 ⋯ 3.1 0.422 1.0 5.5 6.5 2.8 1.3 0.7 3.0 1.8
In [47]:
ggplot(nba.m, aes(variable, Name)) + 
  geom_tile(aes(fill = value), colour = "#D3E3F3") + 
  scale_fill_gradient(low = "#FFFFFF", high = "#08306B") + 
  scale_x_discrete("", expand = c(0, 0)) + 
  scale_y_discrete("", expand = c(0, 0)) + 
  theme_grey(base_size = 9) + 
  theme(legend.position = "none",
        axis.ticks = element_blank(), 
        axis.text.x = element_text(angle = 330, hjust = 0))

# 8.2 multicolor

NOTE: This part runs well in R Studio, but gives an error in Jupyter: feel free to try it out there.

In [48]:
nba.m$Category <- nba.m$variable
levels(nba.m$Category) <- 
  list("Offensive" = c("PTS", "FGM", "FGA", "AST", "X3PM", "X3PA"),
       "Defensive" = c("DRB", "ORB", "TRB","BLK", "PF", "STL"),
       "Other" = c("G", "MIN", "FGP", "FTM", "FTA", "FTP", "X3PP", "TO"))
nba.m$rescaleoffset <- nba.m$value + 100*(as.numeric(nba.m$Category)-1)
scalerange <- range(nba.m$value)
gradientends <- scalerange + rep(c(0,100,200), each=2)

colorends <- c("white", "red", "white", "green", "white", "blue")
In [49]:
# ggplot(nba.m, aes(variable, Name)) + 
#   geom_tile(aes(fill = rescaleoffset), colour = "white") + 
#   scale_fill_gradientn(colours = colorends, values = rescale(gradientends)) + 
#   scale_x_discrete("", expand = c(0, 0)) + 
#   scale_y_discrete("", expand = c(0, 0)) + 
#   theme_grey(base_size = 9) + 
#   theme(legend.position = "none",
#         axis.ticks = element_blank(), 
#         axis.text.x = element_text(angle = 330, hjust = 0))

8.3 multicolor ordered

NOTE: This part runs well in R Studio, but gives an error in Jupyter: feel free to try it out there.

In [50]:
nba.m$variable2 <- reorder(nba.m$variable, as.numeric(nba.m$Category))
In [51]:
# ggplot(nba.m, aes(variable2, Name)) + 
#   geom_tile(aes(fill = rescaleoffset), colour = "white") + 
#   scale_fill_gradientn(colours = colorends, values = rescale(gradientends)) + 
#   scale_x_discrete("", expand = c(0, 0)) + 
#   scale_y_discrete("", expand = c(0, 0)) + 
#   theme_grey(base_size = 9) + 
#   theme(legend.position = "none",
#         axis.ticks = element_blank(), 
#         axis.text.x = element_text(angle = 330, hjust = 0))
In [ ]: